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Semantic Segmentation of Human Model Using Heat Kernel and Geodesic Distance
Joint Authors
Ding, Yongsheng
Hao, Kuangrong
Yang, Huanyu
Source
Mathematical Problems in Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-02-20
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
A novel approach of 3D human model segmentation is proposed, which is based on heat kernel signature and geodesic distance.
Through calculating the heat kernel signature of the point clouds of human body model, the local maxima of thermal energy distribution of the model is found, and the set of feature points of the model is obtained.
Heat kernel signature has affine invariability which can be used to extract the correct feature points of the human model in different postures.
We adopt the method of geodesic distance to realize the hierarchical segmentation of human model after obtaining the semantic feature points of human model.
The experimental results show that the method can overcome the defect of geodesic distance feature extraction.
The human body models with different postures can be obtained with the model segmentation results of human semantic characteristics.
American Psychological Association (APA)
Yang, Huanyu& Hao, Kuangrong& Ding, Yongsheng. 2018. Semantic Segmentation of Human Model Using Heat Kernel and Geodesic Distance. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1209083
Modern Language Association (MLA)
Yang, Huanyu…[et al.]. Semantic Segmentation of Human Model Using Heat Kernel and Geodesic Distance. Mathematical Problems in Engineering No. 2018 (2018), pp.1-13.
https://search.emarefa.net/detail/BIM-1209083
American Medical Association (AMA)
Yang, Huanyu& Hao, Kuangrong& Ding, Yongsheng. Semantic Segmentation of Human Model Using Heat Kernel and Geodesic Distance. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1209083
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1209083